TurkEmbed4Retrieval: Turkish Embedding Model for Retrieval Task

• URL: http://arxiv.org/abs/2511.07595v1
• Date: Wed, 12 Nov 2025 01:06:07 GMT
• Title: TurkEmbed4Retrieval: Turkish Embedding Model for Retrieval Task
• Authors: Özay Ezerceli, Gizem Gümüşçekiçci, Tuğba Erkoç, Berke Özenç,
• Abstract summary: We introduce TurkEmbed4Retrieval, a retrieval specialized variant of the TurkEmbed model.<n>Our model outperforms Turkish colBERT by 19,26% on key retrieval metrics for the Scifact TR dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we introduce TurkEmbed4Retrieval, a retrieval specialized variant of the TurkEmbed model originally designed for Natural Language Inference (NLI) and Semantic Textual Similarity (STS) tasks. By fine-tuning the base model on the MS MARCO TR dataset using advanced training techniques, including Matryoshka representation learning and a tailored multiple negatives ranking loss, we achieve SOTA performance for Turkish retrieval tasks. Extensive experiments demonstrate that our model outperforms Turkish colBERT by 19,26% on key retrieval metrics for the Scifact TR dataset, thereby establishing a new benchmark for Turkish information retrieval.

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